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Direct or Remotely sensed
May be the same data Covariates Direct or Remotely sensed Predictors Remotely sensed Field Data Response, coordinates Sample Data Response, covariates Qualify, Prep Qualify, Prep Qualify, Prep Random split? Randomness Inputs Test Data Training Data Outputs Temp Data Processes Build Model Repeated Over and Over Randomize parameters The Model Statistics Validate Predict Randomness Predicted Values Uncertainty Maps Summarize Predictive Map
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Direct or Remotely sensed
May be the same data Covariates Direct or Remotely sensed Predictors Remotely sensed Jackknife Field Data Response, coordinates Sample Data Response, covariates Qualify, Prep Qualify, Prep Qualify, Prep Random split? Randomness Cross-Validation Noise Injection Inputs Test Data Training Data Outputs Temp Data Processes Build Model Repeated Over and Over Randomize parameters Monte-Carlo The Model Sensitivity Testing Randomness Statistics Validate Predict Noise Injection Predicted Values Uncertainty Maps Summarize Predictive Map
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Cross-Validation Split the data into training (build model) and test (validate) data sets Leave-p-out cross-validation Validate on p samples, train on remainder Repeated for all combinations of p Non-exhaustive cross-validation Leave-p-out cross-validation but only on a subset of possible combinations Randomly splitting into 30% test and 70% training is common
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K-fold Cross Validation
Break the data into K sections Test on 𝐾 𝑖 , Training remainder Repeat for all 𝐾 𝑖 10-fold is common Test 1 2 3 4 Used in rpart() 5 Training 6 7 8 9 10
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Bootstrapping Drawing N samples from the sample data (with replacement) Building the model Repeating the process over and over
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Random Forest N samples drawn from the data with replacement
Repeated to create many trees A “random forest” “Splits” are selected based on the most common splits in all the trees Bootstrap aggregation or “Bagging”
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Boosting Can a set of weak learners create a single strong learner? (Wikipedia) Lots of “simple” trees used to create a really complex tree "convex potential boosters cannot withstand random classification noise,“ 2008 Phillip Long (at Google) and Rocco A. Servedio (Columbia University)
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Boosted Regression Trees
BRTs combine thousands of trees to reduce deviance from the data Currently popular More on this later
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Sensitivity Testing Injecting small amounts of “noise” into our data to see the effect on the model parameters. Plant The same approach can be used to model the impact of uncertainty on our model outputs and to make uncertainty maps Note: I call this noise injection to separate it from sensitivity testing for model parameters
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Jackknifing Trying all combinations of covariates
Given covariates 1,2,3 Try: 1 2 3 1,2 1,3 2,3 1,2, 3
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Building Models Selecting the method
Selecting the covariates/predictors (“Model Selection”) Optimizing the coefficients/parameters of the model 9bytez.com:Old School Hobbies: Building Models by Hand
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Direct or Remotely sensed
May be the same data Covariates Direct or Remotely sensed Predictors Remotely sensed Jackknife Field Data Response, coordinates Sample Data Response, covariates Qualify, Prep Qualify, Prep Qualify, Prep Random split? Randomness Cross-Validation Noise Injection Inputs Test Data Training Data Outputs Temp Data Processes Build Model Repeated Over and Over Randomize parameters Monte-Carlo The Model Sensitivity Testing Randomness Statistics Validate Predict Noise Injection Predicted Values Uncertainty Maps Summarize Predictive Map
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